Method for detecting object and object detecting apparatus

ABSTRACT

A method for detecting an object includes inputting information of a moving object included in a plurality of images and generating a regression tree. In response to input of a new image, the system communicates information of a moving object included in the newly inputted image into the regression tree, and determines a size of a person included in the new image based on a resultant value of the regression tree.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based on and claims priority under 35 U.S.C. §119 toKorean Patent Application No. 10-2014-0124733, filed on Sep. 19, 2014 inthe Korean Intellectual Property Office, the disclosure of which isincorporated herein by reference in its entirety.

BACKGROUND

1. Field

Apparatuses and methods consistent with what is disclosed herein relateto detecting an object, and to an apparatus and a method for detectingan object included in an image rapidly, with rapid and simplecomputation.

2. Description of the Related Art

In order to detect a person in an image, a fixed-size detection windowmay be utilized. In this case, the entire region of the screen isscanned with the detection window. Considering the difficulty ofdetecting a person completely when the size of the person is greaterthan the detection window, the scale of the detection window is variedto find an optimum size. Because no information is given regarding thesize of the object, it is necessary to vary the scale of the detectionwindow. However, such process requires increased computations and timefor detecting an object that includes a person in the image.

Accordingly, a technology is necessary, which can detect an objectincluded in an image rapidly, with fast and simple computations.

SUMMARY

Example embodiments overcome the above disadvantages and otherdisadvantages not described above. Also, example embodiments need not berequired to overcome the disadvantages described above, and exampleembodiments may not overcome any of the problems described above.

According to an embodiment, a technical objective is to provide anapparatus and a method for detecting an object included in an imagerapidly, with rapid and simple computations.

According to an embodiment, a method for detecting an object isprovided, which may include inputting information of a moving objectincluded in a plurality of images and generating a regression tree, inresponse to input of a new image, inputting information of a movingobject included in the newly inputted image into the regression tree,and determining a size of a person included in the new image based on aresultant value of the regression tree.

The generating the regression tree may include identifying movingpixels, using a difference between corresponding pixels of two or moreimages, receiving external input information about the moving pixels,and identifying the external input information according to a presetparameter and generating the regression tree.

The information about the moving pixels may include at least one ofinformation as to whether the moving pixels represent a person or not,location information of the moving pixels, and size information of themoving object.

The identifying the external input information according to the presetparameter and generating the regression tree may include arranging nodeshaving the external input information according to a first parameter andgenerating the regression tree, and when the external input informationof the generated regression tree are not similar to each other,reconstructing the regression tree based on a second parameter which isdifferent from the first parameter.

A leaf node of a final regression tree may include size information ofthe moving object.

The inputting the information of the moving object included in the newlyinputted image may include identifying moving pixels in response to aninput of a plurality of new images, using a difference betweencorresponding pixels of the plurality of newly inputted images, andinputting information about the identified moving pixels into theregression tree.

The information about the identified moving pixels may include locationinformation of the identified moving pixels.

The determining the size of the person included in the new image mayinclude determining if the moving object included in the newly inputtedimage is a person, based on a resultant value of the regression tree,and determining a size of the moving object, when the moving object isthe person.

The method may additionally include setting a detection window scaleaccording to the size of the person.

In an embodiment, an apparatus for detecting an object is provided,which may include a regression tree generator and an object sizedeterminer.

The regression tree generator is configured to input information of amoving object included in a plurality of images and generate aregression tree.

The object size determiner is configured so that in response to input ofa new image, the object size determiner inputs information of a movingobject included in the newly inputted image into the regression tree,and determines a size of a person included in the new image based on aresultant value of the regression tree.

The regression tree generator is configured to identify moving pixels,using a difference between corresponding pixels of two or more images,in response to receiving external input information about the movingpixels, identify the external input information according to a presetparameter and generating the regression tree.

The information about the moving pixels may include at least one ofinformation as to whether the moving pixels represent a person or not,location information of the moving pixels, and size information of themoving object.

Further, the regression tree generator is configured to arrange nodeshaving the external input information according to a first parameter andgenerate the regression tree, and when the external input information ofthe generated regression tree are not similar to each other, reconstructthe regression tree based on a second parameter which is different fromthe first parameter.

A leaf node of a final regression tree may include size information ofthe moving object.

The object size determiner is configured to identify moving pixels inresponse to an input of a plurality of new images, using a differencebetween corresponding pixels of the plurality of newly inputted images,and input information about the identified moving pixels into theregression tree.

The information about the identified moving pixels may include locationinformation of the identified moving pixels.

The object size determiner is configured to determine if the movingobject included in the newly inputted image is a person, based on aresultant value of the regression tree, and determine a size of themoving object, when the moving object is the person.

According to various embodiments, an apparatus and a method are capableof detecting an object included in an image rapidly, with fast andsimple computations.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and/or other aspects of example embodiments will be moreapparent by describing certain example embodiments with reference to theaccompanying drawings, in which:

FIG. 1 illustrates a method for detecting an object using a conventionaldetection window;

FIG. 2 is a flowchart of a detection method according to an exampleembodiment;

FIG. 3 illustrates a method for acquiring a motion history image togenerate a regression tree according to an example embodiment;

FIG. 4 illustrates a motion history image according to an exampleembodiment;

FIG. 5 illustrates a regression tree according to an example embodiment;

FIG. 6 is a flowchart illustrating a method of inputting moving objectinformation of a newly inputted image for regression tree traversal;

FIG. 7 is a flowchart illustrating a method for determining a size of amoving object;

FIGS. 8(A)-8(B) are graphs illustrating a performance of theabove-mentioned method for detecting the object; and

FIG. 9 is a block diagram of a detecting apparatus according to anembodiment.

DETAILED DESCRIPTION

Certain example embodiments will now be described in greater detail withreference to the accompanying drawings.

In the following description, same drawing reference numerals are usedfor the same elements even in different drawings. The matters defined inthe description, such as detailed construction and elements, areprovided to assist in a comprehensive understanding of exampleembodiments. Accordingly, it is apparent that the example embodimentscan be carried out with or without those specifically defined matters.Also, well-known functions or constructions are not described in detailsince they would obscure the description with unnecessary detail.

FIG. 1 illustrates a method for detecting an object using a conventionaldetection window.

A fixed size detection window may be utilized to detect a person in animage. The entire region of the image is scanned with the detectionwindow. When the size of the person is larger than the detection window,it is difficult to detect the person completely. Accordingly, an optimumsize is found, while the scale of the detection window is increased.Because there is no information about the size of the object, therelated technology requires that the scale of the detection window bevaried in sequence, while performing scan over a plurality of scales.However, such operation requires increased computations and time todetect an object including a person.

Example embodiments provide a detection technology, which is capable ofdetermining a size of an object and setting a detection window suitablefor the object rapidly, using a regression tree having size informationof object pixel included in an image. Example embodiments allow a cameraapparatus such as CCTV to set a detection window rapidly with respect toan image including a fixed background representing a specific place anda moving object such as a person. When obtaining a photographed image bysuccessive photography of a specific place, the size of a moving objectsuch as a person moving in the specific place can be predicted based ona location of the object on or in the photographed image. For example,when a camera installed on a corner of a ceiling of a corridor in abuilding is photographing the corridor of the building, as a personmoves closer to the camera from a location farther away from the camera,the object, which appears initially small on or in the photographedimage, increases in size. That is, there is correlation between thelocation on the photographed image and the size of the object. Exampleembodiments generate a regression tree having information about thecorrelation and rapidly determine the size of the object according tothe location of the object on or in the image. When the size of theobject is determined, it is possible to set the corresponding size ofthe detection window.

FIG. 2 illustrates a flowchart of a detection method according to anexample embodiment.

Referring to FIG. 2, at S210, a counting method according to anembodiment first generates a regression tree. Generally, the regressionanalysis represents a method for finding a mathematic formula which canbest or adequately express the given data. The regression treeconstructs a tree that can best or adequately express the given data,while varying parameters. Since obtaining an optimum regression tree(s)is the purpose, this stage can be referred to as a training stage. Whennew data is given, the attributes of the new data may be predicted withthe resultant values of the regression tree.

According to an embodiment, the regression tree is generated byinputting information about the moving object included in a plurality ofsuccessive image frames which make up a video. The regression tree isthus constructed, which can best or adequately express the informationabout the moving object on the successive image frames. This will beexplained in more detail below.

At S220-Y, when a new image is input after the construction of theregression tree, the regression tree is traversed based on theinformation about the image. Specifically, at S230, the information ofthe moving object included in the newly inputted image is input to theregression tree.

According to a result of the regression tree traversal, i.e., accordingto leaf node value, the attributes of the moving object included in thenew image are determined According to an embodiment, it is possible, atS240, to determine the size of a person included in the new image basedon the resultant values of the regression tree. This will be explainedin more detail below.

FIG. 3 illustrates a method for obtaining a motion history image togenerate a regression tree.

The motion history image is an image which is generated by identifying amoving object included in a plurality of successive image frames. Inorder to identify the moving object included in the image frames, atS310, a difference between corresponding pixels between two or moresuccessive images is determined. The region that includes the differencemay be considered as a region where the object is moved. The region isprocessed black, when the pixel value is varied according to movement ofthe object. The rest may be processed white. When all the worksexplained above are completed with respect to the moving objectsincluded in the image, the motion history image as the one illustratedin the right-hand side of FIG. 4 is obtained. FIG. 4 illustrates themotion history image according to an embodiment.

At S320, in the regression tree construction stage, information aboutthe moving object included in the motion history image is input fromoutside. That is, the information about the moving pixels is input, andthis information about the moving pixels may be at least one ofinformation about whether the moving pixels represent a person, locationinformation of the moving pixels, and size information of the movingobject. The location information of the moving pixels may be determinedbased on the coordinate data of the pixels, without requiring externalinput. For the size information of the moving pixels, the size (height)information is inputted from outside regarding the respective movingobjects. At this time, a user may input the size information using aninterface. As a result, the information about whether the objectrepresents a person, the location information of the moving pixels, andthe size information of the moving object including the moving pixels,are stored.

The regression tree is generated using the information about the movingpixels. That is, at S330, the regression tree is generated by dividingthe external input information according to preset parameter(s).

At this time, the step of generating the regression tree by dividing theexternal input information based on the preset parameter(s) may includesteps of generating the regression tree by arranging nodes having theexternal input information according to a first parameter, andreconstructing the regression tree according to a second parameterdifferent from the first parameter, when the external input informationof the leaf nodes of the generated regression tree are not similar toeach other. That is, an optimum regression tree is constructed whileparameters are varied.

In an embodiment, two neighboring pixels of the moving pixels may berandomly chosen. Distances to an object pixel are then calculated. Whenthe distances between the two random pixels to the object pixel arebelow a preset value, the left link of the tree is traversed. On thecontrary, when the distances between the two random pixels to the objectpixel are equal to or greater than the preset value, the right link ofthe tree may be traversed. The verification of the regression tree asdetermined above is then performed. The leaf node of the regression treemay include information about a plurality of moving pixels, in which thepixels about the plurality of moving pixels have to be similar to eachother. For example, it is useful to reconstruct the regression tree,when it is determined that a group of moving pixels (i.e., informationof leaf node) exceptionally include pixels about a person and that sucherror is important. Reconstructing the regression tree is performed byadjusting the parameter values. The above-described process repeatsuntil a regression tree that satisfies a preset error rate isdetermined.

FIG. 4 illustrates a successive image (video) photographed by a camerainstalled on a corner of a ceiling of a building. A motion history imageis obtained with the method explained above, based on the successiveimages photographed by the camera. The size may then be input withrespect to each of the moving objects. It is notable that an object thatis farther away from the camera has a relatively smaller size andlocated at an upper side of the screen, while the object closer to thecamera has a relatively larger size and located at a lower side of thescreen.

FIG. 5 illustrates a regression tree according to an embodiment.

As illustrated, the regression tree is generated with respect to themoving pixels, using inputted information. A specific leaf node includesdominant attribute information of the pixel group. As explained above,the leaf node includes at least one of information about whether thesimilar pixel group (i.e., pixels representing the same object)expresses a person, location information of the pixel group, and sizeinformation of the moving object expressed by the pixel group.

FIG. 6 is a flowchart illustrating a method of inputting moving objectinformation of a newly inputted image for regression tree traversal.

As illustrated in FIG. 6, the step of inputting the information aboutthe moving object included in a newly inputted image into the regressiontree may include, at S610, when a plurality of successive new images isinputted, identifying moving pixels using differences between pixelscorresponding to the plurality of newly inputted images.

At S620, the information about the identified moving pixels is inputtedto the regression tree.

The process is identical to the process of generating a motion historyimage which is described above. That is, the motion history image isgenerated with respect to newly input image. Specifically, a differencebetween corresponding pixels of two or more successive images isdetermined. A region including a difference may be considered to be aregion where the object moves. The region with varied pixels values dueto movement of the object is processed black. The rest may be processedwhite. The above-described operation is performed for all the movingobjects included in the image.

Basically, a moving object has location information. Accordingly, thelocation information of the moving pixel may be input into theregression tree for the traversal of the regression tree. The leaf node,which is a result of the regression tree traversal, representsattributes of the moving pixels. The regression tree traversal allowsfast identification of the attributes of the moving pixels, as theregression tree traversal may be done at log N (when it is assumed thatthere are N number of nodes).

FIG. 7 is a flowchart illustrating a method for determining a size of amoving object.

Referring to FIG. 7, after the regression tree traversal, at S710, it isdetermined as to whether the moving object included in the newlyinputted image is a person or not. At S720-Y, when the object isdetermined to be a person, at S730, the size of the moving object isdetermined. The leaf node provides such information.

As described, when the size of the moving person is detected, thedetection window scale is set accordingly.

As described, detection technology is provided according to anembodiment determines, which can rapidly determine a size of an object,using a regression tree having size information about the object pixelsincluded in an image, and set the detection window that suits the sizeof the object. Specifically, an embodiment allows the system to rapidlyset detection window for an image including a fixed background of aspecific place and a moving object such as a person, at a cameraapparatus such as a CCTV. It is possible to predict the size of anobject moving in a specific place (e.g., person) based on the locationof the object on the photographed image, while the photographed imagesare obtained by successively photographing the specific place. Anembodiment involves generating a regression tree having informationabout correlation between the location on the photographed image and thesize of the object, and rapidly determining the size of the object basedon the location of the object on the image. When the size of the objectis determined, the size of the detection window is then set accordingly.

Meanwhile, the method explained above may be stored in a form of aprogram on a non-transitory computer readable recording medium. Thenon-transitory computer recordable recording medium herein refers to amedium which is capable of semi-permanently storing data, rather thanthose that store data for a brief period of time such as a register orcache, and which can be read by an electronic appliance. For example,the non-transitory recordable readable recording medium may be CD, DVD,hard disk, blu-ray disk, USB, memory card and/or ROM. Further, thecounting method explained above may be provided in a hardware IC chip inthe form of embedded software. It will be appreciated by those skilledin the art that the described systems, methods and techniques may beimplemented in digital electronic circuitry including, for example,electrical circuitry, logic circuitry, hardware, computer hardware,firmware, software, or any combinations of these elements. Apparatusembodying these techniques may include appropriate input and outputdevices, a computer processor, and a computer program product tangiblyembodied in a non-transitory machine-readable storage device or mediumfor execution by a programmable processor. A process embodying thesetechniques may be performed by a programmable hardware processorexecuting a suitable program of instructions to perform desiredfunctions by operating on input data and generating appropriate output.The techniques may be implemented in one or more computer programs thatare executable on a programmable processing system including at leastone programmable processor coupled to receive data and instructionsfrom, and transmit data and instructions to, a data storage system, atleast one input device, and at least one output device. Each computerprogram may be implemented in a high-level procedural or object-orientedprogramming language or in assembly or machine language, if desired; andin any case, the language may be compiled or interpreted language.Suitable processors include, by way of example, both general and specialpurpose microprocessors. Generally, a processor will receiveinstructions and data from a read-only memory and/or a random accessmemory. Non-transitory storage devices suitable for tangibly embodyingcomputer program instructions and data include all forms of computermemory including, but not limited to, non-volatile memory, including byway of example, semiconductor memory devices, such as ErasableProgrammable Read-Only Memory (EPROM), Electrically ErasableProgrammable Read-Only Memory (EEPROM), and flash memory devices;magnetic disks, such as internal hard disks and removable disks;magneto-optical disks; Compact Disc Read-Only Memory (CD-ROM), digitalversatile disk (DVD), Blu-ray disk, universal serial bus (USB) device,memory card, or the like. Any of the foregoing may be supplemented by,or incorporated in, specially designed hardware or circuitry including,for example, application-specific integrated circuits (ASICs) anddigital electronic circuitry. Thus, methods for providing image contentsdescribed above may be implemented by a program including an executablealgorithm that may be executed in a computer, and the program may bestored and provided in a non-transitory computer readable medium.

FIGS. 8(A)-(B) are graphs illustrating a performance of theabove-mentioned method for detecting the object.

The graph FIG. 8(A) indicates that in the straining step, theinformation about the size of the object is input according to thelocation of the object with reference to the camera (i.e., location onthe screen), and that optimum information is obtained by constructing aregression tree. The solid-line curve generally expresses that the sizeof the object decreases as it is farther away from the camera.

The graph FIG. 8(B) shows a size of the object belonging to the leafnode, as a result of generating motion history image of the movingobject included in a plurality of successive image frames and inputtinginformation about the moving pixels into the regression tree for theregression tree traversal. The leaf node represents a size of the objectthat includes a plurality of pixel groups, and it is possible to inputall the moving pixel information constructing a single object andaverage the size values of the objects of the leaf nodes to determine afinal size of the object. The dotted-line curve represents the finallydetermined object size, which is approximate to the solid-line curve.

FIG. 9 is a block diagram of a detecting apparatus 100 according to anembodiment. According to various embodiments, the detection apparatusmay be implemented as at least one of CCTV camera, digital camera,smartphone, server, PC, tablet PC, digital television, and digitalsignage.

Referring to FIG. 9, an apparatus 100 for detecting an object accordingto an embodiment includes a regression tree generator 110 and an objectsize determiner 120.

The regression tree generator 110 is configured to input information ofa moving object included in a plurality of images and generate aregression tree.

The object size determiner 120 is configured so that when a new image isinputted, the object size determiner 120 inputs the information aboutthe moving object included in the newly inputted image into theregression tree and determine a size of a person included in the newimage based on the resultant values of the regression tree.

At this time, the regression tree generator 110 may identify movingpixels using differences between corresponding pixels of two or moreimages, and when information about the moving pixels is inputted fromoutside, identify the external input information based on a presetparameter and generate the regression tree.

Further, the information about the moving pixels may be at least one ofthe information as to whether the moving pixels represent a person ornot, the location information of the moving pixels, and the sizeinformation of the moving objects.

Further, the regression tree generator 110 may arrange nodes having theexternal input information according to the first parameter and generatethe regression tree, and when the external input information of the leafnode of the generated regression tree are not similar to each other, mayreconstruct the regression tree according to the second parameter whichis different from the first parameter.

Further, the leaf node of the final regression tree may include sizeinformation of the moving object.

Further, the object size determiner 120 may determine the moving pixelusing different corresponding pixels of a plurality of new images inresponse to input of a plurality of new images, and may input determinedmoving pixel information into the regression tree.

Further, the determined moving pixel information may include locationinformation about the determined information.

Further, the object size determiner 120 may determine whether or not themoving object included in the newly inputted image is a person, based onthe resultant values of the regression tree, and if determining themoving object to be the person, may determine the size of the movingobject.

Further, the object detecting apparatus 100 may additionally include aphotographer (not illustrated) configured to successively photograph aplurality of images including an object, and the object size determiner120 may set a detection window scale according to the size of theperson. The photographer may include various technical means tophotograph an object. That is, the photographer may include a lens, aniris, an image processor, a storage, a shutter, or an image sensor.

Further, the object detecting apparatus 100 may include a configurationas that of a general electronic calculator. Accordingly, the objectdetecting apparatus 100 may include hardware configuration such as microprocessing unit (MPU) or central processing unit (CPU), a cache memory,a data bus, a storage, or a wired/wireless interface, and softwareconfiguration of an operating system or an application to execute aspecific purpose.

Further, the foregoing example embodiments and advantages are merely forpurposes of example and are not to be construed as limiting the exampleembodiments. The present teaching can be readily applied to other typesof apparatuses. Also, the description of the example embodiments isintended to be illustrative, and not to limit the scope of the claims.

What is claimed is:
 1. A method for detecting an object, the methodcomprising: receiving input information relating to a moving objectincluded in a plurality of images; identifying moving pixels, using adifference between corresponding pixels of two or more images; receivinginformation about the moving pixels; classifying the information aboutthe moving pixels according to a preset parameter and generating aregression tree; in response to receiving a new image, communicatinginformation of a moving object included in the new image into theregression tree; and determining a size of a person included in the newimage based at least on a resultant value of the regression tree.
 2. Themethod of claim 1, wherein the information about the moving pixelscomprises at least one of information as to whether the moving pixelsrepresent a person or not, location information of the moving pixels,and size information of the moving object.
 3. The method of claim 1,wherein the classifying the information about the moving pixelsaccording to the preset parameter and generating the regression treecomprises: arranging nodes having the information about the movingpixels according to a first parameter and generating the regressiontree; and when the information of the generated regression tree is notreciprocally similar, reconstructing the regression tree based on asecond parameter which is different from the first parameter.
 4. Themethod of claim 3, wherein a leaf node of a final regression treecomprises size information of the moving object.
 5. The method of claim1, wherein the determining the size of the person included in the newimage comprises: determining if the moving object included in the newlyinputted image is a person, based on a resultant value of the regressiontree; and determining a size of the moving object, when the movingobject is the person.
 6. The method of claim 1, further comprisingsetting a detection window scale according to the size of the person. 7.A method for detecting an object, the method comprising: receiving inputinformation relating to a moving object included in a plurality ofimages and generating a regression tree; in response to receiving a newimage, communicating information of a moving object included in the newimage into the regression tree; and determining a size of a personincluded in the new image based at least on a resultant value of theregression tree, wherein the communicating the information of the movingobject included in the new image comprises: identifying moving pixels inresponse to information regarding a plurality of new images, using adifference between corresponding pixels of the plurality of new images;and communicating information about the identified moving pixels intothe regression tree.
 8. The method of claim 7, wherein the informationabout the identified moving pixels comprises location information of theidentified moving pixels.
 9. An apparatus for detecting an object,comprising: a regression tree generator configured to input informationof a moving object included in a plurality of images and generate aregression tree; and an object size determiner configured so that inresponse to input of a new image, the object size determiner isconfigured to input information of a moving object included in the newlyinputted image into the regression tree, and determine a size of aperson included in the new image based at least on a resultant value ofthe regression tree, wherein the regression tree generator is configuredto identify moving pixels, using a difference between correspondingpixels of two or more images, in response to receiving external inputinformation about the moving pixels, identify the external inputinformation according to a preset parameter and generate the regressiontree.
 10. The apparatus of claim 9, wherein the information about themoving pixels comprises at least one of information as to whether themoving pixels represent a person or not, location information of themoving pixels, and size information of the moving object.
 11. Theapparatus of claim 9, wherein the regression tree generator isconfigured to arrange nodes having the external input informationaccording to a first parameter and generate the regression tree, andwhen the external input information of the generated regression tree arenot similar to each other, reconstruct the regression tree based on asecond parameter which is different from the first parameter.
 12. Theapparatus of claim 11, wherein a leaf node of a final regression treecomprises size information of the moving object.
 13. The apparatus ofclaim 9, wherein the object size determiner is configured to determineif the moving object included in the newly inputted image is a person,based on a resultant value of the regression tree, and determine a sizeof the moving object, when the moving object is the person.
 14. Theapparatus of claim 9, further comprising a photographing deviceconfigured to photograph the object, and wherein the object sizedeterminer is configured to set a detection window scale according to asize of the person.
 15. An apparatus for detecting an object,comprising: a regression tree generator configured to input informationof a moving object included in a plurality of images and generate aregression tree; and an object size determiner configured so that inresponse to input of a new image, the object size determiner isconfigured to input information of a moving object included in the newlyinputted image into the regression tree, and determine a size of aperson included in the new image based at least on a resultant value ofthe regression tree; wherein the object size determiner is configured toidentify moving pixels in response to an input of a plurality of newimages, using a difference between corresponding pixels of the pluralityof newly inputted images, and input information about the identifiedmoving pixels into the regression tree.
 16. The apparatus of claim 15,wherein the information about the identified moving pixels compriseslocation information of the identified moving pixels.
 17. An apparatusfor detecting an object, comprising: regression tree generator circuitryconfigured to input information of a moving object included in aplurality of images and generate a regression tree; and object sizedeterminer circuitry configured so that in response to input of a newimage, the object size determiner circuitry is configured to inputinformation of a moving object included in the newly inputted image intothe regression tree, and determine a size of a person included in thenew image based at least on a resultant value of the regression tree,wherein the regression tree generator circuitry is configured toidentify moving pixels, using a difference between corresponding pixelsof two or more images, in response to receiving external inputinformation about the moving pixels, identify the external inputinformation according to a preset parameter and generate the regressiontree.